{"data":{"id":"10.6084/m9.figshare.17091653","type":"dois","attributes":{"doi":"10.6084/m9.figshare.17091653","prefix":"10.6084","suffix":"m9.figshare.17091653","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Shen, Ruoque","givenName":"Ruoque","familyName":"Shen","nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4408-829X","nameIdentifierScheme":"ORCID"}],"affiliation":[]},{"name":"Dong, Jie","givenName":"Jie","familyName":"Dong","affiliation":[],"nameIdentifiers":[]},{"name":"Yuan, Wenping","givenName":"Wenping","familyName":"Yuan","affiliation":[],"nameIdentifiers":[]},{"name":"Han, Wei","givenName":"Wei","familyName":"Han","affiliation":[],"nameIdentifiers":[]},{"name":"Ye, Tao","givenName":"Tao","familyName":"Ye","affiliation":[],"nameIdentifiers":[]},{"name":"Zhao, Wenzhi","givenName":"Wenzhi","familyName":"Zhao","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"A 30-m resolution distribution map of maize for China based on Landsat and Sentinel images"}],"publisher":"figshare","container":{},"publicationYear":2024,"subjects":[{"subject":"Agricultural land management","schemeUri":"http://www.abs.gov.au/ausstats/abs@.nsf/0/6BB427AB9696C225CA2574180004463E","subjectScheme":"ANZSRC Fields of Research","classificationCode":"300202"}],"contributors":[],"dates":[{"date":"2024-04-12","dateType":"Created"},{"date":"2024-04-12","dateType":"Updated"},{"date":"2024","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"Cites","relatedIdentifier":"10.34133/2022/9846712","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["2162495558 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"As the second largest producer of maize, China contributes 23% of global maize production and plays an important role in guaranteeing maize markets stability. In spite of its importance, there is no 30 m spatial resolution distribution map of maize for all of China. This study used a time-weighted dynamic time warping method to identify planting areas of maize by comparing the similarity of time series of a satellite-based vegetation index at each pixel with a standard time series derived from known maize fields and mapped maize distribution from 2016 to 2020 over 22 provinces accounting for more than 99% of the maize planting area in China. Based on 18800 field-surveyed pixels at 30-meter spatial resolution, the distribution map yields 76.15% and 81.59% of producer’s and user’s accuracies averaged over the entire investigated provinces, respectively. Municipality- and county-level census data also show a good performance in reproducing the spatial distribution of maize. This study provides an approach to mapping maize over large areas based on a small volume of field survey data.\u003cbr\u003eclassification system:1: maize0: non-maize\u003cbr\u003eRuoque Shen, Jie Dong, Wenping Yuan, Wei Han, Tao Ye, Wenzhi Zhao, \"A 30 m Resolution Distribution Map of Maize for China Based on Landsat and Sentinel Images\", \u003ci\u003eJournal of Remote Sensing\u003c/i\u003e, vol. 2022, Article ID 9846712, 12 pages, 2022. https://doi.org/10.34133/2022/9846712","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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